# ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection

arXiv: https://arxiv.org/abs/2312.14535 · HTML: https://mlsanity.com/paper/2312.14535/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 30 of 30)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Rethinking Graph Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2205.15508/) (2022)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive](https://mlsanity.com/paper/2105.07342/) (2021)
- [Self-supervised Learning on Graphs: Deep Insights and New Direction](https://mlsanity.com/paper/2006.10141/) (2020)
- [Augmentation-Free Self-Supervised Learning on Graphs](https://mlsanity.com/paper/2112.02472/) (2021)
- [AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks](https://mlsanity.com/paper/2002.03665/) (2020)
- [BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs](https://mlsanity.com/paper/2206.10071/) (2022)
- [Contrastive Self-supervised Learning for Graph Classification](https://mlsanity.com/paper/2009.05923/) (2020)
- [Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View](https://mlsanity.com/paper/2212.00535/) (2022)
- [SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks](https://mlsanity.com/paper/1908.03849/) (2019)
- [PyGOD: A Python Library for Graph Outlier Detection](https://mlsanity.com/paper/2204.12095/) (2022)
- [Self-supervised Training of Graph Convolutional Networks](https://mlsanity.com/paper/2006.02380/) (2020)
- [Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks](https://mlsanity.com/paper/2011.07267/) (2020)
- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method](https://mlsanity.com/paper/2210.12941/) (2022)

## Built on by (most cited first, 23 of 23)

- [A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection](https://mlsanity.com/paper/2502.13308/) (2025)
- [Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2412.19108/) (2024)
- [DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector](https://mlsanity.com/paper/2410.06549/) (2024)
- [CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection](https://mlsanity.com/paper/2502.08605/) (2025)
- [Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement](https://mlsanity.com/paper/2406.00987/) (2024)
- [HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection](https://mlsanity.com/paper/2407.21742/) (2024)
- [Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection](https://mlsanity.com/paper/2508.00507/) (2025)
- [CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection](https://mlsanity.com/paper/2605.20032/) (2026)
- [UMGAD: Unsupervised Multiplex Graph Anomaly Detection](https://mlsanity.com/paper/2411.12556/) (2024)
- [Regularized Contrastive Partial Multi-view Outlier Detection](https://mlsanity.com/paper/2408.07819/) (2024)
- [Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective](https://mlsanity.com/paper/2505.18002/) (2025)
- [GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)](https://mlsanity.com/paper/2508.15633/) (2025)
- [AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2601.21171/) (2026)
- [Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality](https://mlsanity.com/paper/2403.09039/) (2024)
- [Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning](https://mlsanity.com/paper/2503.12037/) (2025)
- [Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection](https://mlsanity.com/paper/2506.23469/) (2025)
- [Robust Anomaly Detection with Graph Neural Networks using Controllability](https://mlsanity.com/paper/2507.13954/) (2025)
- [Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection](https://mlsanity.com/paper/2508.10785/) (2025)
- [CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision](https://mlsanity.com/paper/2508.12278/) (2025)
- [GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation](https://mlsanity.com/paper/2509.10869/) (2025)
- [Normality Calibration in Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2510.02014/) (2025)
- [NK-GAD: Neighbor Knowledge-Enhanced Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2604.15668/) (2026)
- [RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2608.16018/) (2026)

## Abstract (arXiv)

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Although existing reconstruction-based methods have achieved considerable success, they may face the textitAnomaly Overfitting and textitHomophily Trap problems caused by the abnormal patterns in the graph, breaking the assumption that normal nodes are often better reconstructed than abnormal ones. Our observations indicate that models trained on graphs with fewer anomalies exhibit higher detection performance. Based on this insight, we introduce a novel two-stage framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns. In the next stage, the decoders are retrained for detection on the original graph, benefiting from the multi-level representations learned in the previous stage. Meanwhile, we propose the node anomaly distribution regularization to further alleviate textitAnomaly Overfitting. We validate the effectiveness of our approach through extensive experiments on both synthetic and real-world datasets.